Availability of large volumes of histology image data coupled with advances in computational power have given rise to the development of novel deep learning methods to effectively tackle many pathology tasks. Successful organization of recent challenge competitions have shown that it is possible to outperform the average expert pathologist's performance leading to the prospect of computer-assisted provision of healthcare [1]}. However, there are several challenges that have arisen in the relatively new field of computational pathology. One of the main challenges in order to use deep learning in a routine clinical setting is the lack of high accuracy with high consistency. In fact, there are already many algorithms for different computational pathology tasks such as cell classification, gland segmentation, tissue phenotyping, tumor detection that have been shown to work well in a laboratory settings, but most are not ready to cope with `pathology image data from the wild'.
There are a few methods that are trained and validated on multiple cohorts. Despite scientifically encouraging results, yet far from being ideal for the real-world challenges [2]}, [3]}, [4]}, [5]}. And so the quest for efficient and effective models for histopathology images that are clinical grade is ongoing and is active area of research for the medical imaging community.
